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Comparison

core vs agent-kernel

Verdict

Pick core if cheshire Cat AI, an AI agent microservice, is built in Python and supports function-calling and vector search, making it suitable for projects that require conversational AI capabilities with these features; pick agent-kernel if agent-kernel provides an operating system for scalable enterprise AI agents, supporting deployment and orchestration at scale with native integration support for MCP and A2A.

Markdown twin · core alternatives · agent-kernel alternatives

GraphCanon updated Sep 27, 2026

6views this month

core logo

core

cheshire-cat-ai/core

★ 3.1kpushed Jul 29, 2026
vs
agent-kernel logo

agent-kernel

yaalalabs/agent-kernel

★ 188pushed Sep 11, 2026

Trust & integrity

Signalcoreagent-kernel
Maintenance
Steady (50d since push)
As of Sep 18, 2026 · github_public_v1
Very active (1d since push)
As of Sep 13, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 13, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Sep 20, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 27, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
core trust reportagent-kernel trust reportTrust methodology

Tagline

core
AI agent microservice
agent-kernel
The Operating System for Scalable Enterprise AI Agents

Stars

core
3.1k
agent-kernel
188

Forks

core
409
agent-kernel
86

Open issues

core
11
agent-kernel
137

Language

core
Python
agent-kernel
Python

Adopt for

core
Cheshire Cat AI, an AI agent microservice, is built in Python and supports function-calling and vector search, making it suitable for projects that require conversational AI capabilities with these features.
agent-kernel
Agent-kernel provides an operating system for scalable enterprise AI agents, supporting deployment and orchestration at scale with native integration support for MCP and A2A.

Persona

core
-
agent-kernel
-

Runtime

core
-
agent-kernel
-

License

core
GPL-3.0, which is a copyleft license requiring that any derivative works be released under the same license.
agent-kernel
Apache-2.0

Last pushed

core
Jul 29, 2026
agent-kernel
Sep 11, 2026

Categories

core
AI Agents
agent-kernel
AI Agents

Trust and health

Maintenance

core
Steady (60%)
agent-kernel
Very active (96%)

Days since push

core
50d
agent-kernel
1d

Open issues (now)

core
11
agent-kernel
137

Stars delta

core
+5 (30d)
agent-kernel
+75 (30d)

Open issues delta

core
+3 (30d)
agent-kernel
+9 (30d)

Full report

core
Trust report
agent-kernel
Trust report

Choose core if…

  • License: core is GPL-3.0, agent-kernel is Apache-2.0.
  • Pricing: The code is free and open-source under GPL-3.0, but any derivative works must also be open-source..
  • Requirements: Min 2 GB RAM; Requires Docker; Requires Docker for deployment..
  • Tags unique to core: ag-ui-protocol, agent, ai, assistant.
  • Use Cheshire Cat AI when you need a microservice that supports function-calling and vector search, as it is specifically designed with these features.

When NOT to use core

  • Avoid using Cheshire Cat AI if your project requires proprietary licensing, as it is licensed under GPL-3.0, which may not be compatible with proprietary software.
  • Do not use it if your project does not require or benefit from function-calling or vector search capabilities, as these are core features of Cheshire Cat AI.
GitHubDetails

Choose agent-kernel if…

  • License: agent-kernel is Apache-2.0, core is GPL-3.0.
  • Requirements: It requires Python versions between 3.12 and 3.13.x.; Supports deployment to various environments such as AWS Lambda, ECS, Azure Functions, or Container Apps via one Terraform module..
  • Tags unique to agent-kernel: a2a, adk, aws, azure.
  • If you require seamless scalability across different cloud providers like AWS and Azure without lock-in or rewrites.

When NOT to use agent-kernel

  • If your project is confined to a single, specific AI framework which doesn't require the flexibility Agent-kernel offers.
  • When you do not have Python version 3.12 - 3.13.x, as it's the required runtime environment.
GitHubDetails

Explore

core trust report →agent-kernel trust report →AI Agents category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: core 3.1k · agent-kernel 188 (synced Sep 20, 2026).

Common questions

What is the difference between core and agent-kernel?
core: AI agent microservice. agent-kernel: The Operating System for Scalable Enterprise AI Agents. See the comparison table for live GitHub stats and shared categories.
When should I choose core over agent-kernel?
Choose core over agent-kernel when License: core is GPL-3.0, agent-kernel is Apache-2.0; Pricing: The code is free and open-source under GPL-3.0, but any derivative works must also be open-source.; Requirements: Min 2 GB RAM; Requires Docker; Requires Docker for deployment.; Tags unique to core: ag-ui-protocol, agent, ai, assistant; Use Cheshire Cat AI when you need a microservice that supports function-calling and vector search, as it is specifically designed with these features.
When should I choose agent-kernel over core?
Choose agent-kernel over core when License: agent-kernel is Apache-2.0, core is GPL-3.0; Requirements: It requires Python versions between 3.12 and 3.13.x.; Supports deployment to various environments such as AWS Lambda, ECS, Azure Functions, or Container Apps via one Terraform module.; Tags unique to agent-kernel: a2a, adk, aws, azure; If you require seamless scalability across different cloud providers like AWS and Azure without lock-in or rewrites.
When should I avoid core?
Avoid using Cheshire Cat AI if your project requires proprietary licensing, as it is licensed under GPL-3.0, which may not be compatible with proprietary software. Do not use it if your project does not require or benefit from function-calling or vector search capabilities, as these are core features of Cheshire Cat AI.
When should I avoid agent-kernel?
If your project is confined to a single, specific AI framework which doesn't require the flexibility Agent-kernel offers. When you do not have Python version 3.12 - 3.13.x, as it's the required runtime environment.
Is core or agent-kernel more popular on GitHub?
core has more GitHub stars (3,086 vs 188). Stars measure visibility, not whether either tool fits your constraints.
Are core and agent-kernel open source?
Yes - both are open-source projects on GitHub (core: GPL-3.0, agent-kernel: Apache-2.0).
Where can I find alternatives to core or agent-kernel?
GraphCanon lists graph-backed alternatives at core alternatives and agent-kernel alternatives (core markdown twin, agent-kernel markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, core or agent-kernel?
core: Steady. agent-kernel: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for core and agent-kernel?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: core trust report; agent-kernel trust report.

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